Recognizing an object (e.g., a person or a car) by means of a single recognition system employing a facial recognition camera or license plate recognition system can lead to a high error rate due to varying environmental conditions or partial object occlusion.
This invention proposes a data fusion system that combines the results of many image capture devices or detectors as well as travel time of the object between them in order to improve the recognition rate.
For this purpose, the recognition task is considered a classification problem, where a class corresponds to a particular object from a black list. The classification problem is solved by way of a Bayesian fusion algorithm, which operates on learned statistical models of recognition scores, travel time, and prior information. All these statistical models are generated machine learning algorithms from training data and can be combined in arbitrary fusion using modular data structure.
It will be appreciated that for clarity figure elements may not be been drawn to scale and reference numerals may be repeated among the figures indicating corresponding or analogous elements.